https://ijr.kntu.ac.ir/ International Journal of Robotics, Theory and Applications en daily 1 Wed, 01 Jan 2025 00:00:00 +0330 Wed, 01 Jan 2025 00:00:00 +0330 https://ijr.kntu.ac.ir/article_212357.html Controlling continuum robotic arms presents significant challenges due to their highly nonlinear nature and inherently uncertain and complex structure. This complexity affects the application of continuum arms in various areas such as routing, maneuvering on complex paths, and other applications. This paper addresses a real-time kinematic control of continuum robotic arms using screw theory to develop a controller that offers accuracy, speed, and low computational load for real-time implementation. The inherent flexibility and nonlinear nature of these arms complicate precise position control. To overcome these challenges, we use a PID controller, enhancing the robot's position control capabilities. Experimentally validated results for the designed path demonstrate the controller's effectiveness in improving path tracking and real-time control performance. This controller was implemented on the actual RoboArm system, achieving a 6cm error. https://ijr.kntu.ac.ir/article_226725.html This paper assesses the performance of a lightweight, compact, portable, and wearable cable-driven robotic hand prosthesis called PARS (Prosthesis Adaptive Robotic System). Initially, a mathematical model was developed, followed by the design and fabrication of a Neuro controller. The functionality of the robotic hand was confirmed through both simulations and experimental tests. The pilot study demonstrated that the proposed Neuro controller effectively tracks various desired joint trajectories and performs well in real-world applications. Experimental results indicated a strong correlation between the robotic hand prosthesis (RHP) and the human hand in terms of vertical position, speed, and acceleration during flexion and extension. The Neuro controller surpassed traditional PID in stability and accuracy. This study underscores the practical potential of developing advanced tools for assisting disabled individuals. However, further improvements are needed to enhance practicality, such as integrating force sensors and making the hardware more compact. Additionally, improving the control software to support simultaneous position and active force control could boost system performance in more complex tasks. https://ijr.kntu.ac.ir/article_233034.html Multiple Impedance Control (MIC) in Space Free-Flying Robot (SFFR) is necessary to ensure simultaneous accurate tracking and safe interactions; however, the computation related to grasp in these interactions has become a computational bottleneck, which intensifies with increasing Degrees of Freedom (DoF) and makes on-line control and real-time implementation difficult. Although Machine Learning-based Multiple Impedance Control (ML-MIC) has partly reduced this computational burden, the design of the Machine Learning (ML) network still relies on trial-and-error and does not guarantee optimal reduction of computations. In this paper, an Optimal Machine Learning-based Multiple Impedance Control (OML-MIC) is presented, in which the explicit computation related to the grasp matrix is replaced with a nonlinear approximation based on a Radial Basis Function Neural Network (RBFNN), and the network architecture is optimized using a Genetic Algorithm (GA) to minimize computational cost under accuracy constraints. The proposed method systematically determines the optimal network structure and, while preserving the physical dynamics of the grasp, eliminates the need for heavy linear-algebra operations.Simulation results for planar manipulation of an object by a dual-arm SFFR show that, while meeting the MIC control specifications, OML-MIC reduces the number of multiplication operators by 73.15%, the number of addition operators by 41.27%, and the hidden-layer computations of ML-MIC by 50%. As a result of the structured optimal design, error analysis with statistical quantitative metrics confirms that accuracy remains within the safe-interaction range. These results indicate that integrating architecture optimization with learning-based control provides a reliable path to precise, real-time interaction on robotic platforms with limited resources. https://ijr.kntu.ac.ir/article_233836.html In this paper, a lower limb extremity exoskeleton robot is presented to support people with disabilities in the walking process and rehabilitation. The first section presents the conceptual design of the robot model, which indicates that the robot has seven degrees of freedom. Then the dynamic model of the mechanical system of the exoskeleton has been shown. In order to dynamic simulation, the mechanical model is transferred from the CATIA to MATLAB and simulated in SimMechanics, by applying the system parameters and implementing a complete process of gait cycle. In the following, a combinative controller is designed based on the described system. Finally, the gained results planted on the prototype of the presented system and the given parameters are tested in a loop by placing the control system hardware in a real-time situation. And the results of this approach demonstrated a good response from the control hardware output of the learning system for the semi-active mode in the exoskeleton. https://ijr.kntu.ac.ir/article_245248.html In human–machine interaction, continuous assessment of attentional states enables the development of responsive and adaptive interfaces. However, current approaches often rely on subjective or intrusive measures, limiting their applicability in real-world systems. To address this, we focus on mindful attention, a cognitive state characterized by present-moment awareness and nonreactive focus, which reflects stable, internally regulated attention during interactive tasks. While most studies assess mindfulness through self-report questionnaires, behavioural metrics can provide objective, complementary insights. This study introduces a framework that integrates Trail Making Test (TMT) performance, dynamic mouse movement features, and self-reported mindful attention (MAAS) scores. Mouse trajectory features such as velocity, acceleration, and curvature were extracted and analyzed using multiple regression models, combined with different feature selection strategies. Results showed that wrapper-based and Boruta-style methods substantially outperformed filter-based approaches, with progressive feature selection yielding the highest accuracy. The Gradient Boosting Regressor with progressive selection achieved a strong predictive performance (R^2score = 0.85 on test data), demonstrating that mouse dynamics can serve as reliable behavioural indicators of mindfulness. These findings highlight the potential of integrating behavioural features and machine learning for multidimensional attention assessment. https://ijr.kntu.ac.ir/article_245249.html Video capsule endoscopy is an innovative technique that enables the clinical evaluation of the Gastro-Intestinal tract through a non-invasive approach. Two primary types of video capsules are available: passive and active. Magnetically Driven Video Capsule Endoscopy, is an advanced diagnostic active video capsule that revolutionizes the visualization of the Gastro-Intestinal tract. This method employs an external magnetic field to activate the capsule and facilitate the observation of the gastrointestinal tract. This article will provide a comprehensive review of magnetically driven video capsules, including a focus on external navigation systems for capsule locomotion. Experimental investigations, containing technical and/or clinical tests, will be described. The goal of this review is to emphasize the effectiveness of magnetic fields in propelling video capsules. Also, the efficacy of this device in terms of mobility and disease detection will be assessed based on technical and clinical trial outcomes. The results showed that the mechanical features, such as rotational speed, movement speed, amount of capsule rotation, velocity, frequency and amplitude of vibration of capsule were measured through the technical tests. Experimental results indicate the successful use of the magnetic field to drive and rotate the capsule.